I have been using some version of this approach since my IIM Udaipur days.
Over time, I have realised that most people do not struggle because there is a shortage of information. In fact, we have exactly the opposite problem.
There are too many courses. Too many YouTube videos. Too many books, newsletters, podcasts, Substacks and LinkedIn posts telling us what we should learn.
The real problem is that we consume much more than we convert into actual capability.
You can watch 50 videos about finance without being able to value a company.
You can complete three AI courses without having built anything useful.
You can read every CAT strategy post on the internet without becoming better at taking a CAT mock.
And you can study marketing for months without having actually marketed anything.
That is why I increasingly think of self-education as a simple three-step loop:
Learn → Do → Teach
Learn something from a good source.
Apply it to something real.
Then explain it to someone else.
Do this repeatedly, and something interesting happens. You don’t just become more knowledgeable. You start becoming good at learning itself.
And that is probably one of the most valuable career skills you can build.
Step 1: Learn — Choose a Source and Commit to It
The first step seems obvious: you need to learn.
But there is an important difference between learning and endlessly collecting learning resources.
Suppose you want to understand finance.
You search YouTube and immediately find hundreds of playlists. Then somebody recommends a Coursera course. Somebody else sends you a Substack. You discover a good book. Then you see another creator explaining the same concept differently.
A week later, you have 17 saved resources and have completed none of them.
This happens because choosing learning material feels productive. But after a point, it becomes another form of procrastination.
Find a good-enough source
Your source could be:
- an online course,
- a YouTube playlist,
- a book,
- a professor,
- somebody’s blog,
- a Substack,
- a structured certification,
- or even a combination of two or three sources.
It does not need to be the world’s best resource.
It needs to be good enough and structured enough for you to follow consistently.
Once you have found that source, commit.
If you are learning financial valuation, finish the valuation module.
If you are learning AI, complete the section on building agents.
If you are preparing for consulting, complete one particular framework or type of case.
If you are preparing for CAT, finish Arithmetic before jumping randomly between Arithmetic, Algebra, Geometry and Number Systems.
Give your learning a deadline
I particularly like learning in short cycles.
For example:
“For the next two weeks, this is what I am learning.”
That makes learning finite.
Instead of saying:
“I want to learn finance.”
Say:
“Over the next two weeks, I want to understand financial statements and basic valuation.”
Instead of:
“I need to learn AI.”
Say:
“Over the next two weeks, I want to learn enough about AI automation to build one useful workflow.”
That change sounds small, but it forces clarity.
You now know what you are learning, where you are learning it from, and roughly when you are supposed to finish it.
But learning alone is still not enough.
That brings us to the second part of the loop.
Step 2: Do — Turn Knowledge Into Proof
This is where most learning systems break.
We confuse understanding something with being able to do it.
They are not the same thing.
You can understand a professor explaining a discounted cash flow model and still struggle when somebody gives you an actual company and asks you to value it.
You can understand a tutorial on ChatGPT APIs and still have no idea how to build something useful from scratch.
You can understand a marketing framework and still freeze when somebody asks:
“Okay. How would you grow this company?”
The solution is simple:
Every learning cycle must produce a project.
If you are learning AI, build something
Do not stop at understanding prompting, agents, APIs or automation.
Build.
Maybe you build:
- an AI research assistant,
- a small automation for your work,
- a chatbot,
- an internal knowledge base,
- a simple app,
- or a workflow that saves you 30 minutes every day.
The project does not have to become a startup.
Its job is to force you to use what you have learnt.
If you are learning finance, analyse a company
Take an actual listed company.
Download its annual report.
Understand the income statement, balance sheet and cash-flow statement.
Calculate ratios.
Make assumptions.
Build a valuation.
Suddenly finance is no longer a collection of formulas.
It becomes a way of thinking.
If you are learning marketing, market something
Pick a company.
Analyse its customers.
Build its segmentation.
Think through its positioning.
Create a campaign.
Write the landing page.
Run a small experiment.
You will probably learn more from trying to acquire your first 100 users than from another 20 hours of marketing lectures.
If you are learning consulting, solve cases
Reading frameworks is learning.
Solving cases is doing.
Take an actual business problem and structure it.
What is the problem?
What data would you require?
What hypotheses would you make?
How would you arrive at a recommendation?
The more cases you solve, the more the frameworks slowly disappear from your conscious thinking and become part of the way you naturally approach problems.
And if you are preparing for CAT, take mocks
For CAT aspirants, the equivalent of a project is very simple:
Mocks.
You can study Quant, VARC and DILR for months.
But eventually you need to perform under a 120-minute constraint.
A CAT mock tells you whether your learning survives contact with reality.
Can you select the right questions?
Can you manage time?
Can you leave a difficult question?
Can you recover after a bad section?
Can you maintain accuracy under pressure?
That is the “Do” part of CAT preparation.
The principle is the same in every field:
Don’t just learn the skill. Create an environment in which you are forced to use the skill.
Step 3: Teach — Explain What You Have Learnt
Now comes the part that I think is severely underrated.
Teach.
Once you have learnt something and used it, develop an obligation to explain it.
You don’t need to become a professor.
You don’t even need an audience.
You can teach one person.
But ideally, I would recommend publishing your learning online.
Why?
Because teaching exposes gaps that learning and doing sometimes hide.
You may think you understand something until somebody asks:
“Why?”
Or:
“Can you explain that simply?”
That is when you realise whether your knowledge is actually clear.
Teaching forces clarity
Try explaining EBITDA to somebody who has never studied finance.
Try explaining positioning without using marketing jargon.
Try explaining why a particular CAT question should have been left rather than attempted.
Try explaining how an AI workflow actually works.
To explain something simply, you first need to organise it inside your own head.
That is why teaching is not something you do after mastering a subject.
Teaching is part of how you master it.
And today, teaching can also become distribution
You can share what you learn through:
- LinkedIn posts,
- Instagram,
- YouTube,
- X,
- your own blog,
- a newsletter,
- Substack,
- or even small communities.
Imagine that every week you learn one concept, apply it somewhere and publish what you discovered.
After one year, you have not merely “studied” for one year.
You have created around 50 pieces of public evidence showing how you think.
That is incredibly powerful.
The Hidden Benefit: You Build a Portfolio While You Learn
This is where the Learn–Do–Teach system becomes much more powerful than a normal study plan.
Imagine two MBA students who both say:
“I am interested in finance.”
Student A has completed four finance courses.
Student B has:
- completed two good courses,
- analysed five listed companies,
- built three valuation models,
- written articles explaining those companies,
- and published a video explaining how valuation assumptions change the final price.
Who would you believe understands finance better?
More importantly, who has more evidence?
That is the difference between claiming a skill and demonstrating a skill.
Your “Do” creates proof of work.
Your “Teach” makes that proof visible.
And together, they slowly create a portfolio.
The Second Hidden Benefit: You Accidentally Build a Personal Brand
People sometimes make personal branding unnecessarily complicated.
They wonder:
“What should I post?”
You don’t necessarily need to manufacture content.
Document your learning.
If you are learning consulting, share a case you solved.
If you are learning marketing, analyse a campaign.
If you are learning finance, explain a company.
If you are experimenting with AI, show something you built.
Your personal brand then becomes an outcome of becoming better at your craft.
This is a much stronger form of personal branding because you are not trying to look knowledgeable.
You are publicly showing the process through which you are becoming knowledgeable.
Over time, people begin associating you with certain subjects.
And that can lead to opportunities that are difficult to predict in advance: jobs, freelance projects, consulting assignments, collaborations, clients or even businesses.
Why the Three Steps Need Each Other
The power is not in any individual step.
It is in the loop.
Learn without doing
You become a consumer of information.
You know concepts, but struggle to apply them.
Do without learning
You may improve through trial and error, but progress can be unnecessarily slow because you keep reinventing things that others have already understood.
Learn and do without teaching
You can become competent, but you miss two benefits: the deeper clarity that comes from explaining your thinking and the career leverage that comes from making your work visible.
But when all three happen together:
Learn → Do → Teach → Learn again
you create a compounding system.
Teaching exposes a weakness.
That weakness tells you what to learn next.
You learn it.
You apply it.
You teach the new insight.
And the cycle repeats.
A Simple Two-Week Learn–Do–Teach System
If you want to implement this immediately, don’t create a six-month curriculum.
Pick one skill and run a two-week experiment.
Days 1–7: Learn
Define exactly what you want to understand.
Choose one primary source.
Study it properly.
Take notes.
Avoid constantly changing resources.
Days 8–12: Do
Build something.
Solve something.
Analyse something.
Apply the concepts to a real problem.
This is where the messy learning happens.
Days 13–14: Teach
Create one piece explaining what you learnt.
It could be:
- a LinkedIn post,
- a short video,
- a detailed blog,
- a YouTube video,
- a presentation,
- a Twitter/X thread,
- or simply a conversation with someone else.
Then ask yourself:
What did I struggle to explain?
That becomes the starting point of your next learning cycle.
Your Goal Should Be to Become Great at Learning
The world of work is changing too quickly for education to end with college.
You will probably have to learn new tools, skills and domains throughout your career.
AI today.
Something else tomorrow.
That means the biggest advantage may not be knowing one particular skill.
It may be developing the confidence that:
“Give me something important, and I know how to teach myself.”
That is what Learn–Do–Teach can eventually give you.
You stop waiting for somebody to design the perfect course.
You stop believing you need another degree every time you enter a new domain.
You become more comfortable entering subjects you know nothing about because you have a process.
Find a good source.
Learn.
Build something.
Teach what you learnt.
Find the gaps.
Repeat.
Do this enough times and you will not just become better at AI, finance, marketing, consulting or CAT.
You will become dangerously good at self-education.
What This Looks Like Across Different Skills
The easiest way to understand this framework is to see how it changes depending on what you are trying to learn. The “Do” should create some form of proof that you can actually use the skill, while the “Teach” should force you to explain your thinking clearly to somebody else.
| What you are learning | LEARN — What should you study? | DO — What project should you build? | TEACH — How should you explain/share it? |
|---|---|---|---|
| AI & Generative AI | Pick one structured source and understand prompting, workflows, agents, automation and whichever tools are relevant to your work. Don’t try to learn every AI tool available; focus on understanding what problems AI can actually solve. | Build something that you will genuinely use. For example, create an AI research assistant, automate your content research, build a simple chatbot, create an automated reporting workflow, or build a small tool using APIs or no-code platforms. | Record a screen-share showing what you built and how it works. You could also write a LinkedIn post explaining the problem, your approach, what did not work, and what you learnt while building it. |
| Finance | Learn financial statements first, followed by ratios, corporate finance, valuation and financial modelling. Use one good course or book instead of jumping between dozens of finance creators. | Pick a listed company and analyse it properly. Read its annual report, understand its business model, create a financial model and eventually attempt a DCF or comparable-company valuation. | Publish a simple company analysis: “How does Zomato actually make money?” or “I valued this company and here are the three assumptions that mattered most.” Explaining finance without jargon will also test whether you really understand it. |
| Marketing | Learn STP, consumer behaviour, branding, acquisition, funnels, pricing and basic marketing metrics. Alongside theory, regularly study how actual companies position and market themselves. | Pick a real company and create a complete marketing diagnosis. Identify its target consumer, analyse its positioning, audit its funnel and then propose a campaign or growth experiment that you would actually run. | Turn your analysis into a case study. You could post “If I were the marketing manager of this brand, here are three things I would change” and explain the reasoning behind each recommendation. |
| Consulting / Case Solving | Learn problem structuring, hypothesis-driven thinking, basic business frameworks, market sizing and case interview fundamentals. The goal is not to memorise 50 frameworks but to learn how to break an unclear problem into smaller questions. | Solve real business problems rather than only reading solved cases. Pick a company and answer questions such as why its growth has slowed, whether it should enter a new market, or how it could improve profitability. | Publish your problem-solving structure. Explain the problem, the hypotheses you considered, the data you would need and the recommendation you eventually reached. You can even teach a case to another student and make them challenge your assumptions. |
| CAT Preparation | Learn the concepts required for Quant, VARC and DILR from a reliable source. But concept learning should gradually become a smaller part of preparation as you become comfortable with the syllabus. | Your project is the mock test. A mock forces you to combine knowledge, question selection, speed, accuracy, temperament and time management in one real testing environment. | Analyse your mock and teach the lessons from it. You could explain why you selected certain questions, where you wasted time, how you solved a difficult DILR set, or what you would do differently in the next mock. |
| Excel / Data Analysis | Learn formulas, lookups, PivotTables, data cleaning, charts and eventually more advanced functions depending on your role. Learn each feature around an actual business problem rather than memorising formulas separately. | Download a real dataset and create something useful from it. Build a sales dashboard, analyse customer data, create a budget model, or turn messy raw information into a management report. | Create a short tutorial around the project rather than teaching random Excel tricks. For example: “I took 50,000 rows of sales data and built this dashboard — here is exactly how I approached it.” |
| Product Management | Learn customer discovery, problem identification, prioritisation, product metrics, experimentation and basic UX thinking. Study actual products alongside the theory so you understand why product decisions are made. | Choose an existing product and redesign one specific experience. Speak to a few users, identify a problem, create a proposed solution, prepare wireframes and define the metrics you would use to judge whether the feature works. | Publish the complete product teardown. Explain what problem you found, how you validated it, what you would build and why you rejected other possible solutions. |
| Sales | Learn prospecting, qualification, discovery, objection handling, negotiation and closing. But sales is one of those skills where passive learning becomes useless very quickly. | Actually try selling something. It could be your own service, a small digital product, an event, freelance work or even a hypothetical B2B solution where you conduct real discovery conversations with potential buyers. | Share what happened in those conversations. Explain the objections people raised, which questions opened up the conversation, why prospects said no and what you changed in your pitch after speaking to them. |
| Entrepreneurship | Learn customer discovery, business models, pricing, distribution, unit economics and basic operations. Avoid spending months learning entrepreneurship without ever interacting with a potential customer. | Build the smallest possible version of a business. Create a landing page, speak with 20 potential customers, offer the service manually and try to get your first paying customer before building something complicated. | Document the journey openly. Talk about your hypothesis, what customers actually told you, what you initially got wrong and how the business changed after real market feedback. |
| Coding / Software Development | Learn the fundamentals of one language and then move into whichever stack is required for what you want to build. Avoid doing tutorial after tutorial without ever starting something independently. | Build a working application. It does not have to be revolutionary — a habit tracker, expense manager, Chrome extension, job application tracker or simple SaaS tool is enough if you genuinely build it yourself. | Write a build log or create a walkthrough. Explain the architecture, the bugs you encountered, why you made certain choices and what you would build differently in version two. |
| Data Science / Analytics | Learn statistics, SQL, Python, visualisation and basic modelling in a logical sequence. More importantly, understand how data is used to answer business questions rather than treating analytics as only a technical skill. | Take a public dataset and answer an actual question with it. For example, analyse customer churn, predict demand, study pricing behaviour or identify which variables seem to influence an important outcome. | Publish the analysis as a business story rather than merely uploading code. Explain the question you started with, what the data revealed, what limitations existed and what decision you would recommend based on the analysis. |
| Public Speaking | Study storytelling, presentation structure, delivery, body language and how strong speakers hold attention. Watching great talks is useful, but speaking is ultimately a performance skill. | Prepare and deliver actual talks. Start with a five-minute explanation, record yourself, review it and then repeat the same talk several times while deliberately fixing one problem at a time. | Teaching is naturally built into public speaking. Run a small session for friends, colleagues or an online audience where you teach a topic you already know, then review the recording and feedback. |
| Writing / Content Creation | Study clear writing, storytelling, hooks, structure, editing and the writing styles of people you admire. Reading good writing is an important part of learning how good writing works. | Publish consistently rather than keeping everything in drafts. Write 10 LinkedIn posts, five essays, a newsletter series or one detailed research article and observe what makes people continue reading. | Teach the ideas behind your writing process. Break down why you used a particular hook, how you structured an argument, or what you learnt after publishing 30 pieces of content. |
| Strategy | Learn industry analysis, competitive advantage, business models, market dynamics and strategic decision-making. Case studies become particularly useful because strategy makes much more sense when studied through actual companies. | Pick a company facing a major strategic question. Analyse its industry, competitors, economics and capabilities, and then write a recommendation on what you believe the company should do over the next three years. | Convert the analysis into a short strategy memo, presentation or public article. Teaching forces you to defend not just what you recommend, but why this choice is better than the alternatives. |
| Communication / Business Communication | Learn how to structure arguments, write concise emails, create presentations and communicate recommendations clearly. Study strong business writing and notice how much unnecessary information good communicators remove. | Take a complicated business problem and create a one-page memo or five-slide executive presentation explaining it. Your objective should be that a senior leader can understand the situation and decision quickly. | Present the memo to another person and ask them to explain your recommendation back to you. If they misunderstood your point, that is evidence that your communication still needs improvement. |
The pattern is almost always the same
Whatever skill you choose, your learning cycle should end with an output.
If you learnt finance, there should eventually be a valuation. If you learnt marketing, there should be a campaign or market analysis. If you learnt AI, there should be something that works. If you learnt consulting, there should be a solved business problem. If you prepared for CAT, there should be mocks and mock analysis.
And then there should be an explanation of that output.
That explanation could be a LinkedIn post, YouTube video, Instagram Reel, blog, Substack article, presentation, workshop or simply a conversation with another person. The platform is secondary.
The important question is:
Can I explain what I did, why I did it, what happened, and what I learnt from it?
If you can repeatedly move from knowledge → application → explanation, you are no longer simply consuming education. You are building capability, proof of work and eventually a body of work that other people can see.
